Abstract
The ever-increasing demand for a reliable inference capable of handling unpredictable challenges of practical application in the real world has made research on information fusion of major importance; indeed, this challenge is pervasive in a whole range of image understanding tasks. In the development of the most common type—score-level fusion algorithms—it is virtually universally desirable to have as a reference starting point a simple and universally sound baseline benchmark which newly developed approaches can be compared to. One of the most pervasively used methods is that of weighted linear fusion. It has cemented itself as the default off-the-shelf baseline owing to its simplicity of implementation, interpretability, and surprisingly competitive performance across a widest range of application domains and information source types. In this paper I argue that despite this track record, weighted linear fusion is not a good baseline on the grounds that there is an equally simple and interpretable alternative—namely quadratic mean-based fusion—which is theoretically more principled and which is more successful in practice. I argue the former from first principles and demonstrate the latter using a series of experiments on a diverse set of fusion problems: classification using synthetically generated data, computer vision-based object recognition, arrhythmia detection, and fatality prediction in motor vehicle accidents. On all of the aforementioned problems and in all instances, the proposed fusion approach exhibits superior performance over linear fusion, often increasing class separation by several orders of magnitude.
Highlights
Score-level fusion of information is pervasive in a wide variety of problems
From predictions of the price of French vintage wine using the fusion of predictions based on rainfall and temperature data [1], to sophisticated biometrics algorithms which fuse similarity measures based on visual, infrared face appearance, or gait characteristics [2,3,4,5], the premise of information fusion is the same: the use of multiple information sources facilitates the making of better decisions [6,7]
Proposed quadratic mean-based fusion results in lower fused matching scores than those obtained by employing linear fusion
Summary
Score-level fusion of information is pervasive in a wide variety of problems. From predictions of the price of French vintage wine using the fusion of predictions based on rainfall and temperature data [1], to sophisticated biometrics algorithms which fuse similarity measures based on visual, infrared face appearance, or gait characteristics [2,3,4,5], the premise of information fusion is the same: the use of multiple information sources facilitates the making of better decisions [6,7]. The effectiveness of a specific information fusion methodology needs to be demonstrated. If possible this should be done by comparing its performance against the current state-of-the-art. In other cases there may not be a clear state-of-the-art because the particular problem at hand has not been addressed before In such circumstances it is useful to compare the novel methodology with a simple yet sensible baseline [8]. The simplest choices for the baseline would be the performances achieved using individual information sources which are being fused. The proposed alternative is inherently more principled than the aforementioned baseline, while being no more complex, either computationally or conceptually
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